crop_yield_prediction/train_cross_location.py [132:189]:
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    n_batches = len(validation_dataloader)
    n_samples = len(validation_dataloader.dataset)
    batch_size = validation_dataloader.batch_size

    predictions = torch.zeros(n_samples)
    # collect y as batch_y has been shuffled
    y = torch.zeros(n_samples)

    sum_loss_dic = {}
    for loss_type in ['loss', 'loss_supervised', 'loss_unsupervised',
                      'l_n', 'l_d', 'l_nd', 'sn_loss', 'tn_loss', 'norm_loss']:
        sum_loss_dic[loss_type] = 0

    with torch.no_grad():
        for i, (batch_X, batch_y) in enumerate(validation_dataloader):
            batch_X, batch_y = prep_data(batch_X, batch_y, cuda)

            # forward
            emb_triplets, pred = model(batch_X, unsup_weight)

            loss_func = torch.nn.MSELoss()
            loss_supervised = loss_func(pred, batch_y)

            if unsup_weight != 0:
                loss_unsupervised, l_n, l_d, l_nd, sn_loss, tn_loss, norm_loss = triplet_loss(emb_triplets,
                                                                                   tilenet_margin, tilenet_l2, tilenet_ltn)

                loss = (1 - unsup_weight) * loss_supervised + unsup_weight * loss_unsupervised
            else:
                loss = loss_supervised

            start = i * batch_size
            end = start + batch_size if i != n_batches - 1 else n_samples
            predictions[start:end] = pred
            y[start:end] = batch_y

            sum_loss_dic['loss'] += loss.item()
            sum_loss_dic['loss_supervised'] += loss_supervised.item()
            if unsup_weight != 0:
                sum_loss_dic['loss_unsupervised'] += loss_unsupervised.item()
                sum_loss_dic['l_n'] += l_n.item()
                sum_loss_dic['l_d'] += l_d.item()
                sum_loss_dic['l_nd'] += l_nd.item()
                sum_loss_dic['sn_loss'] += sn_loss.item()
                sum_loss_dic['tn_loss'] += tn_loss.item()
                if tilenet_l2 != 0:
                    sum_loss_dic['norm_loss'] += norm_loss.item()

    if cuda:
        predictions, y = predictions.cpu(), y.cpu()
        predictions, y = predictions.data.numpy(), y.data.numpy()

    rmse, r2, corr = cal_performance(predictions, y)
    avg_loss_dic = {}
    for loss_type in sum_loss_dic.keys():
        avg_loss_dic[loss_type] = sum_loss_dic[loss_type] / n_batches

    return avg_loss_dic, rmse, r2, corr
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crop_yield_prediction/train_semi_transformer.py [140:197]:
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    n_batches = len(validation_dataloader)
    n_samples = len(validation_dataloader.dataset)
    batch_size = validation_dataloader.batch_size

    predictions = torch.zeros(n_samples)
    # collect y as batch_y has been shuffled
    y = torch.zeros(n_samples)

    sum_loss_dic = {}
    for loss_type in ['loss', 'loss_supervised', 'loss_unsupervised',
                      'l_n', 'l_d', 'l_nd', 'sn_loss', 'tn_loss', 'norm_loss']:
        sum_loss_dic[loss_type] = 0

    with torch.no_grad():
        for i, (batch_X, batch_y) in enumerate(validation_dataloader):
            batch_X, batch_y = prep_data(batch_X, batch_y, cuda)

            # forward
            emb_triplets, pred = model(batch_X, unsup_weight)

            loss_func = torch.nn.MSELoss()
            loss_supervised = loss_func(pred, batch_y)

            if unsup_weight != 0:
                loss_unsupervised, l_n, l_d, l_nd, sn_loss, tn_loss, norm_loss = triplet_loss(emb_triplets,
                                                                                   tilenet_margin, tilenet_l2, tilenet_ltn)

                loss = (1 - unsup_weight) * loss_supervised + unsup_weight * loss_unsupervised
            else:
                loss = loss_supervised

            start = i * batch_size
            end = start + batch_size if i != n_batches - 1 else n_samples
            predictions[start:end] = pred
            y[start:end] = batch_y

            sum_loss_dic['loss'] += loss.item()
            sum_loss_dic['loss_supervised'] += loss_supervised.item()
            if unsup_weight != 0:
                sum_loss_dic['loss_unsupervised'] += loss_unsupervised.item()
                sum_loss_dic['l_n'] += l_n.item()
                sum_loss_dic['l_d'] += l_d.item()
                sum_loss_dic['l_nd'] += l_nd.item()
                sum_loss_dic['sn_loss'] += sn_loss.item()
                sum_loss_dic['tn_loss'] += tn_loss.item()
                if tilenet_l2 != 0:
                    sum_loss_dic['norm_loss'] += norm_loss.item()

    if cuda:
        predictions, y = predictions.cpu(), y.cpu()
        predictions, y = predictions.data.numpy(), y.data.numpy()

    rmse, r2, corr = cal_performance(predictions, y)
    avg_loss_dic = {}
    for loss_type in sum_loss_dic.keys():
        avg_loss_dic[loss_type] = sum_loss_dic[loss_type] / n_batches

    return avg_loss_dic, rmse, r2, corr
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